Abstract
This work mines big data in Sentinel-1 satellite images to unveil geographical patterns in offshore wind energy. We leverage unsupervised machine learning to extract insights from a 44GB open access dataset for decision support in wind farm orientations to guide stakeholders. It has broader impacts of overcoming climate change by enhancing renewable energy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
| Editors | Wei Ding, Chang-Tien Lu, Fusheng Wang, Liping Di, Kesheng Wu, Jun Huan, Raghu Nambiar, Jundong Li, Filip Ilievski, Ricardo Baeza-Yates, Xiaohua Hu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 8778-8780 |
| Number of pages | 3 |
| ISBN (Electronic) | 9798350362480 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE International Conference on Big Data, BigData 2024 - Washington, United States Duration: 15 Dec 2024 → 18 Dec 2024 |
Publication series
| Name | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
|---|---|
| ISSN (Print) | 2639-1589 |
| ISSN (Electronic) | 2573-2978 |
Conference
| Conference | 2024 IEEE International Conference on Big Data, BigData 2024 |
|---|---|
| Country/Territory | United States |
| City | Washington |
| Period | 15/12/24 → 18/12/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy -
SDG 13 Climate Action
Keywords
- Climate Change
- Clustering
- Geospatial Big Data
- Image Mining
- Ocean Wind Field
- Radar
- Renewable Energy
- Satellite Data
- Unsupervised Learning
- World Geodetic System
Fingerprint
Dive into the research topics of 'Mining Satellite Imagery for Offshore Wind Energy'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver